Correct recognition of obstacles is of great significance to unmanned vehicles and autonomous driving modes of vehicles. When implementing automatic recognition of the obstacles, lidar sensors, millimeter-wave lidar sensors or image capturing apparatuses are generally installed on the vehicles to gather obstacle information around the vehicles to obtain three-dimensional point cloud data or two-dimensional image data. Next, the obstacles in the three-dimensional point cloud data or the two-dimensional image data are recognized by utilizing trained machine learning algorithms. When training the machine learning algorithms, generally the three-dimensional point cloud data or the two-dimensional image data in which the obstacles have been labeled are utilized to train the machine learning algorithms.
Existing labeling methods mainly utilize a manually labeling manner, which not only consumes manpower and financial resources, but also reduces the labeling efficiency.